{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Principal component analysis (PCA)\n",
    "\n",
    "* [1. What is PCA?](#intro-pca)\n",
    "* [2. Why do we need PCA?](#why-pca)\n",
    "* [3. Key ideas of PCA?](#key-ideas)\n",
    "* [4. Two ways of deriving PCA](#derivation)\n",
    "    * [4.1 Maximum variance perspective](#max-var-perspective)\n",
    "    * [4.2 Projection perspective](#proj-perspective)\n",
    "\n",
    "* [5. Interpreting the projection matrix](#interpretation)\n",
    "* [6. Computing the eigenvectors](#comp-eigenvec)\n",
    "* [7. Summary of important terminology](#terminology)\n",
    "* [8. Implementation](#implementation)\n",
    "    * [8.1 Dataset](#dataset)\n",
    "    * [8.2 Standardization](#standardization)\n",
    "    * [8.3 Computing the data covariance matrix](#comp-cov-matrix)\n",
    "    * [8.4 Computing eigenvectors and eigenvalues](#comp-eig-vecs)\n",
    "    * [8.5 Choosing the number of eigenvalues](#num-eig-vals)\n",
    "    * [8.6 Constructing the projection matrix](#proj-matrix)\n",
    "    * [8.7 Projecting the data](#proj-data)\n",
    "    * [8.8 Plotting the transformed data](#plot-data)\n",
    "    * [8.9 Comparison to scikit-learn](#sklearn-comparison)\n",
    "\n",
    "* [9. Sources and further reading](#sources)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Link to interactive demo\n",
    "\n",
    "[Click here](https://mybinder.org/v2/gh/zotroneneis/machine_learning_basics/HEAD?labpath=principal_component_analysis.ipynb) to run the notebook online (using Binder) without installing jupyter or downloading the code.\n",
    "\n",
    "Sometimes, the GitHub version of the Jupyter notebook does not display the math formulas correctly. Please refer to the Binder version in case you think something might be off or missing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. What is PCA? <a class=\"anchor\" id=\"intro-pca\"></a>\n",
    "\n",
    "In simple terms, principal component analysis (PCA) is a technique to perform dimensionality reduction. It has been around for more than 100 years and is still heavily used. Although PCA is most often applied to find a lower-dimensional representation of data, it can also be used for other purposes, e.g. to detect simple patterns in data. \n",
    "\n",
    "## 2. Why do we need PCA? <a class=\"anchor\" id=\"why-pca\"></a>\n",
    "A lot of data is high-dimensional. Imagine patient data collected in a hospital. For each patient we can have hundreds or even thousands of measurements (blood pressure, heart rate, respiratory rate, etc.). Working with such data is difficult - it's expensive to store, hard to analyze and almost impossible to visualize.\n",
    "\n",
    "Luckily, many dimensions in high-dimenstional data are often redundant and can be expressed by combining some of the other dimensions. Also, the key part of the data is often contained in a more compact lower-dimensional structure. Consequently, we can simplify high-dimensional datapoints using dimensionality reduction techniques like PCA.\n",
    "\n",
    "## 3. Key ideas of PCA <a class=\"anchor\" id=\"key-ideas\"></a>\n",
    "- PCA finds a lower-dimensional representation of data by constructing new features (called principal components) which are linear combinations of the original features\n",
    "- The new features are selected carefully: PCA looks for features that can summarize the data best without losing too much information"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Two ways of deriving PCA  <a class=\"anchor\" id=\"derivation\"></a>\n",
    "\n",
    "Let's say we have an i.i.d dataset $\\boldsymbol{X}$ with $D$ dimensions and a mean value of $0$:\n",
    "$\\mathcal{\\boldsymbol{X}} = \\{\\boldsymbol{x}_1, ..., \\boldsymbol{x}_N\\}$, $\\boldsymbol{x}_n \\in \\mathbb{R}^D$. The data covariance matrix (which will be needed later on) is computed as follows:\n",
    "\n",
    "$\\boldsymbol{S} = \\frac{1}{N} \\sum_{n=1}^{N} \\boldsymbol{x}_n \\boldsymbol{x}_n^T$\n",
    "\n",
    "In PCA our goal is to find projections of the datapoints $\\boldsymbol{x}_n$ that are as similar to the original datapoints as possible but have lower dimensionality. We can approach this goal from two perspectives:\n",
    "\n",
    "1. Maximum variance perspective: We try to find a low-dimensional representation which maximizes the variance of the projected data.\n",
    "2. Projection perspective: We try to find a low-dimensional representation which minimizes the average reconstruction error between the original data and the reconstructed data.\n",
    "\n",
    "Both approaches reach the same result."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Maximum variance perspective  <a class=\"anchor\" id=\"max-var-perspective\"></a>\n",
    "\n",
    "In the maximum variance perspective PCA is derived as an algorithm that tries to find a transformation matrix $\\boldsymbol{B}$ which projects the original data $\\boldsymbol{X}$ to a low-dimensional space, ideally without losing information. Let's say that this low-dimensional space has dimension $M$. \n",
    "\n",
    "How can we make sure that we find a matrix $B$ that retains as much information as possible? It turns out that this is achieved when $\\boldsymbol{B}$ projects the data in a way that maximizes the variance of the data in the (new) low-dimensional space.\n",
    "\n",
    "Let's take a closer look at $\\boldsymbol{B}$. We can define $\\boldsymbol{B}$ as a collection of vectors $\\boldsymbol{b}_1, ..., \\boldsymbol{b}_M$:\n",
    "\n",
    "$\\boldsymbol{B} := [\\boldsymbol{b}_1, ..., \\boldsymbol{b}_M] \\in \\mathbb{R}^{D x M}$\n",
    "\n",
    "The vectors $\\boldsymbol{b}_m$ are called *basis vectors* and form the axes of the new $M$-dimensional space we project our data to.\n",
    "\n",
    "When deriving which vectors $\\boldsymbol{b}_m$ we should select to maximize the variance we will find that maximizing the variance is equivalent to selecting those vectors that belong to the largest eigenvalues of the data covariance matrix $\\boldsymbol{S}$. That means that we can construct our matrix $\\boldsymbol{B}$ by first computing the eigenvalues and eigenvectors of the covariance matrix $\\boldsymbol{S}$ and then selecting the $M$ eigenvectors with the largest eigenvalues.\n",
    "\n",
    "To be more precise: $\\boldsymbol{b}_1$ will be the eigenvector with the largest eigenvalue. In the context of PCA it's called the *first principal component*. $\\boldsymbol{b}_2$ will be the eigenvector with the second largest eigenvalue and is called *second principal component* and so on. \n",
    "\n",
    "If you are interested in the derivation take a look at chapter 10.2 of the book [Mathematics for Machine Learning](https://mml-book.com).\n",
    "\n",
    "Once we have found our projection matrix $\\boldsymbol{B}$ we can transform each data vector $\\boldsymbol{x}_n$ by multiplying it with $\\boldsymbol{B}$. This will give us a low-dimensional compressed representation of $\\boldsymbol{x}_n$:\n",
    "\n",
    "$\\boldsymbol{z}_n = \\boldsymbol{B}^T \\boldsymbol{x}_n \\in \\mathbb{R}^M$\n",
    "\n",
    "Using matrix multiplications we could represent the computation as follows:\n",
    "\n",
    "<img src=\"figures/pca_matrix_multiplications.png\" width=\"600\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Projection perspective  <a class=\"anchor\" id=\"proj-perspective\"></a>\n",
    "\n",
    "Another way to derive PCA is to consider the original data points $\\boldsymbol{x}_n$ and their reconstructions $\\boldsymbol{\\tilde{x}}_n$. In this perspective we are trying to find reconstructions $\\boldsymbol{\\tilde{x}}_n$ that minimize the averaged squared Euclidean distance between the original datapoints and their reconstructions: $J = \\frac{1}{N} \\sum_{n=1}^{N}||\\boldsymbol{x}_n - \\boldsymbol{\\tilde{x}}_n ||^2$.\n",
    "\n",
    "You can find more details about this perspective in chapter 10.3 of the book [Mathematics for Machine Learning](https://mml-book.com)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Interpreting the projection matrix  <a class=\"anchor\" id=\"interpretation\"></a>\n",
    "\n",
    "In the beginning I mentioned that PCA constructs new features (the principal components) that are linear combinations of the original features. Let's take a closer look at what this means.\n",
    "\n",
    "Each datapoint is stored in a vector with $D$ elements: $\\boldsymbol{x}_n = [x_{n1}, ..., x_{nD}]$. Each of the $D$ dimensions represents a different feature. For example, think of an image with $D = 64$ pixels. We can describe each datapoint as a linear combinations of the features: $\\boldsymbol{x}_n = x_{n1} \\cdot \\text{feature}_1 + x_{n2} \\cdot \\text{feature}_2 + ... + x_{nD} \\cdot \\text{feature}_D$\n",
    "\n",
    "Using the example of pixels this would correspond to:\n",
    "$\\boldsymbol{x}_n = x_{n1} \\cdot \\text{pixel}_1 + x_{n2} \\cdot \\text{pixel}_2 + ... + x_{nD} \\cdot \\text{pixel}_D$\n",
    "\n",
    "When PCA projects the data to a low-dimensional space it also uses a combination of the features. This becomes evident in the projection equation we have seen above:\n",
    "\n",
    "$\\boldsymbol{z}_n = \\boldsymbol{B}^T \\boldsymbol{x}_n \\in \\mathbb{R}^M$\n",
    "\n",
    "In this equation we find the compressed representation $\\boldsymbol{z}_n$ of the datapoint $\\boldsymbol{x}_n$ by performing a matrix multiplication. The new, compressed representation of a datapoint $\\boldsymbol{x}_n$ will be given by a linear combination of all its features values. We can make this more clear when considering an example.\n",
    "\n",
    "Let's say our data matrix $\\boldsymbol{X}$ has 3 datapoints and 2 features, so  $\\boldsymbol{X} \\in \\mathbb{R}^{3x2}$ and we consider only the first principal component when performing PCA. Hence we have  $\\boldsymbol{B} \\in \\mathbb{R}^{2x1}$. When multiplying the data matrix with the projection matrix we receive the compressed versions of the datapoints as a matrix $\\boldsymbol{Z} \\in \\mathbb{R}^{3x1}$. Each one-dimensional code $\\boldsymbol{z}_n$ is given by a linear combination of the original feature values:\n",
    "\n",
    "<img src=\"figures/linear_combinations.png\" width=\"800\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Computing the eigenvectors  <a class=\"anchor\" id=\"comp-eigenvec\"></a>\n",
    "\n",
    "By now we know that we have to compute eigenvalues and eigenvectors to perform PCA. We have to ways to do that: \n",
    "\n",
    "1. Perform an eigendecomposition of the data covariance matrix $\\boldsymbol{S}$\n",
    "2. Perform a singular value decomposition of the data matrix $\\boldsymbol{X}$\n",
    "\n",
    "The standard approach (which we will use in this tutorial) is approach one. You can find more on approach two in chapter 10.4 of the [Mathematics for Machine Learning](https://mml-book.com) book.\n",
    "\n",
    "Note: in many applications we only need the first few eigenvectors. Performing a full eigendecomposition or singular value decomposition would be computationally wasteful. Therefore, most software packages implement iterative methods which directly optimize the required number of eigenvectors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Summary of important terminology <a class=\"anchor\" id=\"terminology\"></a>\n",
    "\n",
    "- $\\boldsymbol{x}_n$ are our datapoints, stored in the matrix $\\mathcal{\\boldsymbol{X}} = \\{\\boldsymbol{x}_1, ..., \\boldsymbol{x}_N\\}$, $\\boldsymbol{x}_n \\in \\mathbb{R}^D$\n",
    "- $\\boldsymbol{S}$ is the data covariance matrix \n",
    "- $\\boldsymbol{B}$ is the projection matrix and consists of column vectors $\\boldsymbol{b}_m$: $\\boldsymbol{B} := [\\boldsymbol{b}_1, ..., \\boldsymbol{b}_M] \\in \\mathbb{R}^{D x M}$\n",
    "- $\\boldsymbol{z}_n$ is the low-dimensional representation of $\\boldsymbol{x}_n$: $\\boldsymbol{z}_n = \\boldsymbol{B}^T \\boldsymbol{x}_n \\in \\mathbb{R}^M$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Implementation <a class=\"anchor\" id=\"implementation\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_digits\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.1 Dataset  <a class=\"anchor\" id=\"dataset\"></a>\n",
    "\n",
    "We will use the digits dataset from scikit-learn for our implemetation. It contains 8x8 images of handwritten digits (1797 in total). The data is stored in a data matrix with 1797 rows and 64 columns (corresponding to all 8x8=64 pixel values)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of data matrix: (1797, 64)\n",
      "Shape of label matrix: (1797,)\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "digits = load_digits()\n",
    "data, labels = digits.data, digits.target\n",
    "\n",
    "print(f\"Shape of data matrix: {data.shape}\")\n",
    "print(f\"Shape of label matrix: {labels.shape}\")\n",
    "\n",
    "# Example image\n",
    "plt.imshow(digits.images[42], cmap=\"gray\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.2 Standardization  <a class=\"anchor\" id=\"standardization\"></a>\n",
    "\n",
    "Before we can apply PCA we should standardize the data. If you are wondering why this is necessary [check out this explanation on StackExchange](https://stats.stackexchange.com/questions/69157/why-do-we-need-to-normalize-data-before-principal-component-analysis-pca). Standardization rescales the data to have mean zero and standard deviation one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean = np.mean(data, axis=0)\n",
    "std = np.std(data, axis=0)\n",
    "# If values have a standard deviation of zero, the normalization step\n",
    "# will contain a division by zero. To prevent this we replace standard\n",
    "# deviations of zero with one.\n",
    "std[std == 0] = 1.\n",
    "data_norm = (data - mean) / std\n",
    "\n",
    "# We could also perform standardization using scikit learn\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "data_norm_sklearn = StandardScaler().fit_transform(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.3 Computing the data covariance matrix  <a class=\"anchor\" id=\"comp-cov-matrix\"></a>\n",
    "\n",
    "To find the principal components we first have to compute the data covariance matrix $\\boldsymbol{S}$:\n",
    "\n",
    "$\\boldsymbol{S} = \\frac{1}{N} \\sum_{n=1}^{N} \\boldsymbol{x}_n \\boldsymbol{x}_n^T$\n",
    "\n",
    "We can summarize the summation as follows:\n",
    "\n",
    "$\\boldsymbol{S} = \\frac{1}{N} \\boldsymbol{X}^T \\boldsymbol{X}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_samples, n_features = data_norm.shape\n",
    "cov_matrix = (data_norm.T.dot(data_norm)) / (n_samples)\n",
    "\n",
    "# We could also compute the covariance matrix with numpy (see code below)\n",
    "# Note: the numpy implementation divides by n_samples - 1. This\n",
    "# is known as Bessel's correction. Refer to the corresponding \n",
    "# Wikipedia article if interested in more details.\n",
    "cov_matrix_numpy = np.cov(data_norm.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.4 Computing the eigenvalues and eigenvectors  <a class=\"anchor\" id=\"comp-eig-vals\"></a>\n",
    "\n",
    "To compute eigenvalues and eigenvectors we perform an eigendecomposition of the covariance matrix $\\boldsymbol{S}$. Doing this manually is messy and it's hard to do the computations correctly. Therefore, we will make use of numpy to get both eigenvalues and eigenvectors. If you want to learn more about how to perform an eigendecomposition take a look at the [Wikipedia article](https://en.wikipedia.org/wiki/Eigendecomposition_of_a_matrix#Numerical_computations).\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of eigenvalue matrix: (64,)\n",
      "Shape of eigenvectors matrix: (64, 64)\n",
      "=====================================\n",
      "Eigenvalue: 7.340688819618325\n",
      "Eigenvalue: 5.832243185889725\n",
      "Eigenvalue: 5.151093084500989\n"
     ]
    }
   ],
   "source": [
    "eig_vals, eig_vecs = np.linalg.eig(cov_matrix)\n",
    "print(f\"Shape of eigenvalue matrix: {eig_vals.shape}\")\n",
    "print(f\"Shape of eigenvectors matrix: {eig_vecs.shape}\")\n",
    "print(\"=====================================\")\n",
    "\n",
    "# We want to find the eigenvectors with the highest eigenvalues. \n",
    "# To do so, we use argsort which returns the indices that would \n",
    "# sort the array of eigenvalues. Since we want to sort from largest\n",
    "# to lowest value we need to reverse the order of the result\n",
    "sorted_indices = np.argsort(eig_vals)[::-1]\n",
    "\n",
    "# Look at the first few eigenvalues to make sure that we sorted them correctly\n",
    "for idx in sorted_indices[:3]:\n",
    "    print(f\"Eigenvalue: {eig_vals[idx]}\")   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.5 Choosing the number of eigenvalues <a class=\"anchor\" id=\"num-eig-vals\"></a>\n",
    "\n",
    "In order to decide how many eigenvalues we want to use we should ask ourselves what our goal is. Remember that the number of eigenvalues that we select corresponds to the dimensionality of the new low-dimensional space we are creating.\n",
    "\n",
    "Most of the time, PCA is used to reduce the dimensionality of data. But at the same time we want to make sure that our solution is good, meaning that we don't lose a lot of information. Consequently, we should try to select the eigenvalues that explain most of the variance in the data.\n",
    "\n",
    "Typically, the first few eigenvalues capture most of the variance whereas later ones don't add much information. When using tools like scikit-learn we can specify how much variance should be explained by the solution so we don't have to choose the number of eigenvalues ourselves.\n",
    "\n",
    "In our case we can decide how many principal components to use by computing the *explained variance* of the individual eigenvectors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sum_eig_vals = sum(eig_vals)\n",
    "sorted_eig_vals = sorted(eig_vals, reverse=True)\n",
    "explained_variance = [(eig_val / sum_eig_vals) *  100 for eig_val in sorted_eig_vals]\n",
    "cumulative_explained_variance = np.cumsum(explained_variance)\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.bar(range(len(eig_vals)), explained_variance, label=\"Explained variance\", color=\"red\")\n",
    "plt.xlabel(\"Index of principal components\")\n",
    "plt.ylabel(\"Explained variance in percent\")\n",
    "plt.title(\"Variance captured by the individual principal components\")\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.bar(range(len(eig_vals)), cumulative_explained_variance, label=\"Cumulative explained variance\", color=\"blue\")\n",
    "plt.xlabel(\"Number of principal components\")\n",
    "plt.ylabel(\"Cumulative explained variance in percent\")\n",
    "plt.title(\"Cumulative variance captured by the principal components\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The upper plot shows that the first principal components explain more variance than latter ones. However, the amount of variance explained is not very high. Consequently, we would lose a lot of information when using only the first few principal components. The lower plot shows that we would have to keep 31 principal components to explain 90% of the variance. This is a lot but still better than keeping all 64 values.\n",
    "\n",
    "For the purpose of this tutorial we will keep only the 2 largest eigenvalues and their corresponding eigenvectors. This will allow us to visualize the result in two dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Eigenvalues shape: (2,)\n",
      "Eigenvectors shape: (64, 2)\n"
     ]
    }
   ],
   "source": [
    "# Select only the number of eigenvalues / principal components that we want to use\n",
    "n_principal_comps = 2\n",
    "eig_vals = eig_vals[sorted_indices[:n_principal_comps]]\n",
    "eig_vecs = eig_vecs[:, sorted_indices[:n_principal_comps]]\n",
    "\n",
    "print(f\"Eigenvalues shape: {eig_vals.shape}\")\n",
    "print(f\"Eigenvectors shape: {eig_vecs.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.6 Constructing the projection matrix  <a class=\"anchor\" id=\"proj-matrix\"></a>\n",
    "\n",
    "After finding the eigenvectors/principal components we can arrange them into a matrix (our *projection matrix* $\\boldsymbol{B}$)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of projection matrix: (64, 2)\n"
     ]
    }
   ],
   "source": [
    "vector_list = [vec.reshape(n_features, 1) for vec in eig_vecs.T]\n",
    "proj_matrix = np.hstack(vector_list) # This is our projection matrix B\n",
    "print(f\"Shape of projection matrix: {proj_matrix.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.7 Projecting the data  <a class=\"anchor\" id=\"proj-data\"></a>\n",
    "\n",
    "In order to actually project the original data $\\boldsymbol{X}$ into the lower-dimensional space we multiply it with the matrix $\\boldsymbol{B}$:\n",
    "\n",
    "$\\boldsymbol{Z} = \\boldsymbol{X} \\boldsymbol{B} \\in \\mathbb{R}^{N x M}$\n",
    "\n",
    "<img src=\"figures/pca_digits_matrix_multiplications.png\" width=\"600\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of projected data matrix: (1797, 2)\n"
     ]
    }
   ],
   "source": [
    "data_proj = data_norm.dot(proj_matrix) # This is our compressed data matrix Z\n",
    "print(f\"Shape of projected data matrix: {data_proj.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.8 Plotting the transformed data  <a class=\"anchor\" id=\"plot-data\"></a>\n",
    "\n",
    "With only two principal components we can plot the transformed data easily. One axis will correspond to the first principle component, the other axis to the second."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels_str = [\"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"]\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "scatter = plt.scatter(data_proj[:, 0], data_proj[:, 1], c=labels)\n",
    "plt.legend(handles=scatter.legend_elements()[0], labels=labels_str, loc=\"upper left\")\n",
    "plt.xlabel('Principal Component 1')\n",
    "plt.ylabel('Principal Component 2')\n",
    "plt.title(\"Compressed digit dataset with labels\")\n",
    "plt.show();\n",
    "\n",
    "# Stackoverflow post to add legend: \n",
    "# https://stackoverflow.com/questions/17411940/matplotlib-scatter-plot-legend"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.9 Comparison to scikit-learn  <a class=\"anchor\" id=\"sklearn-comparison\"></a>\n",
    "\n",
    "In practice it doesn't make sense to perform PCA manually. Instead, we can use packages like scikit-learn. This simplifies the computation substantially. Let's apply PCA as contained in scikit-learn to the digits dataset and compare it to our solution. As visibile in the plot below, our implementation of PCA gives the same result as the scikit-learn version!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "sklearn_pca = PCA(n_components=2)\n",
    "data_proj_sklearn = sklearn_pca.fit_transform(data_norm)\n",
    "# The eigenvectors in scikit-learn will point into the opposite direction\n",
    "# compared to our eigenvectors (in other words: they are flipped). This\n",
    "# does not make a difference regarding the solution. However, to make \n",
    "# sure that we get exactly the same plot we multiply the result by -1.\n",
    "data_proj_sklearn *= -1\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "scatter = plt.scatter(data_proj_sklearn[:, 0], data_proj_sklearn[:, 1], c=labels, label=labels_str)\n",
    "plt.xlabel('Principal Component 1')\n",
    "plt.ylabel('Principal Component 2')\n",
    "plt.legend(handles=scatter.legend_elements()[0], labels=labels_str, loc=\"upper left\")\n",
    "plt.title(\"Compressed digits dataset with labels (sklearn version)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. Sources and further reading <a class=\"anchor\" id=\"sources\"></a> \n",
    "\n",
    "The basis for this notebook is chapter 10 of the book [Mathematics for Machine Learning](https://mml-book.com). I can highly recommend to read through the chapter to get a deeper understanding of PCA.\n",
    "\n",
    "Another fantastic explanation of PCA is given [in this post on StackExchange](https://stats.stackexchange.com/a/140579). "
   ]
  }
 ],
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   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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